This paper gives a generalization of results presented by ten Berge, Krijnen, Wansbeek & Shapiro. They examined procedures and results as proposed by Anderson & Rubin, McDonald, Green and Krijnen, Wansbeek & ten Berge. We shall consider the same matter, under weaker rank assumptions. We allow some moments, namely the variance Ω of the observable scores vector and that of the unique factors Ψ to be singular. We require T'Ψ T > 0 where T Λ T' is a Schur decomposition of Ω. As usual the variance of the common factors Φ, and the loadings matrix A will have full column rank
AbstractWe report a matrix expression for the covariance matrix of MLEs of factor loadings in factor...
The classical fitting problem in exploratory factor analysis (EFA) is to find estimates for the fact...
From the literature three types of predictors for factor scores are available. These are characteriz...
This paper gives a generalization of results presented by ten Berge, Krijnen, Wansbeek & Shapiro. Th...
Anderson and Rubin and McDonald have proposed a correlation-preserving method of factor scores predi...
AbstractAnderson and Rubin and McDonald have proposed a correlation-preserving method of factor scor...
Factor score predictors are computed when individual factor scores are of interest. Conditions for a...
summary:The author shows that a decomposition of a covariance matrix $\bold{\sum = AA'}$ implies the...
AbstractThis paper characterises completely the circumstances in which maximum likelihood estimation...
We report a matrix expression for the covariance matrix of MLEs of factor loadings in factor analysi...
In this paper we present a simple approach to factor analysis to estimate the true correlations betw...
AbstractIn reduced-rank regression, a matrix of expectations is modeled as a lower rank matrix. In f...
AbstractIn an approach aiming at high-dimensional situations, we first introduce a distribution-free...
We consider multi-set data consisting of inline image observations, k = 1,…, K (e.g., subject scores...
AbstractThis paper is concerned with the asymptotic covariance matrix (ACM) of maximum-likelihood es...
AbstractWe report a matrix expression for the covariance matrix of MLEs of factor loadings in factor...
The classical fitting problem in exploratory factor analysis (EFA) is to find estimates for the fact...
From the literature three types of predictors for factor scores are available. These are characteriz...
This paper gives a generalization of results presented by ten Berge, Krijnen, Wansbeek & Shapiro. Th...
Anderson and Rubin and McDonald have proposed a correlation-preserving method of factor scores predi...
AbstractAnderson and Rubin and McDonald have proposed a correlation-preserving method of factor scor...
Factor score predictors are computed when individual factor scores are of interest. Conditions for a...
summary:The author shows that a decomposition of a covariance matrix $\bold{\sum = AA'}$ implies the...
AbstractThis paper characterises completely the circumstances in which maximum likelihood estimation...
We report a matrix expression for the covariance matrix of MLEs of factor loadings in factor analysi...
In this paper we present a simple approach to factor analysis to estimate the true correlations betw...
AbstractIn reduced-rank regression, a matrix of expectations is modeled as a lower rank matrix. In f...
AbstractIn an approach aiming at high-dimensional situations, we first introduce a distribution-free...
We consider multi-set data consisting of inline image observations, k = 1,…, K (e.g., subject scores...
AbstractThis paper is concerned with the asymptotic covariance matrix (ACM) of maximum-likelihood es...
AbstractWe report a matrix expression for the covariance matrix of MLEs of factor loadings in factor...
The classical fitting problem in exploratory factor analysis (EFA) is to find estimates for the fact...
From the literature three types of predictors for factor scores are available. These are characteriz...